rwkv-architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill rwkv --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RWKV - Receptance Weighted Key Value ## Quick start RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference). **Installation**: ```bash # Install PyTorch pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121 # Install dependencies pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade # Install RWKV pip install rwkv ``` **Basic usage** (GPT mode + RNN mode): ```python import os from rwkv.model import RWKV os.environ["RWKV_JIT_ON"] = '1' os.environ["RWKV_CUDA_ON"] = '1' # Use CUDA kernel for speed # Load model model = RWKV( model='/path/to/RWKV-4-Pile-1B5-20220903-8040', strategy='cuda fp16' ) # GPT mode (parallel processing) out, state = model.forward([187, 510, 1563, 310, 247], None) print(out.detach().cpu().numpy()) # Logits # RNN mode (sequential processing, same result) out, state = model.forward([187, 510], None) # First 2 tokens out, state = model.forward([1563], state) # Next token out, state = model.forward([310, 247], state) # Last tokens print(out.detach().cpu().numpy()) # Same logits as above! ``` ## Common workflows ### Workflow 1: Text generation (streaming) **Efficient token-by-token g
- Quick start
- Common workflows
- Workflow 1: Text generation (streaming)
- Workflow 2: Long context processing (infinite context)
- Workflow 3: Fine-tuning RWKV
- Workflow 4: RWKV vs Transformer comparison
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Install PyTorch pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121 Install dependencies pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade Install RWKV pip install rwkv
What does the rwkv-architecture skill do?
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill rwkv --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.